def rhf_internal(mf, with_symmetry=True, verbose=None):
log = logger.new_logger(mf, verbose)
g, hop, hdiag = newton_ah.gen_g_hop_rhf(mf,
mf.mo_coeff,
mf.mo_occ,
with_symmetry=with_symmetry)
hdiag *= 2
def precond(dx, e, x0):
hdiagd = hdiag - e
hdiagd[abs(hdiagd) < 1e-8] = 1e-8
return dx / hdiagd
# The results of hop(x) corresponds to a displacement that reduces
# gradients g. It is the vir-occ block of the matrix vector product
# (Hessian*x). The occ-vir block equals to x2.T.conj(). The overall
# Hessian for internal reotation is x2 + x2.T.conj(). This is
# the reason we apply (.real * 2) below
def hessian_x(x):
return hop(x).real * 2
x0 = numpy.zeros_like(g)
x0[g != 0] = 1. / hdiag[g != 0]
if not with_symmetry: # allow to break point group symmetry
x0[numpy.argmin(hdiag)] = 1
e, v = lib.davidson(hessian_x, x0, precond, tol=1e-4, verbose=log)
if e < -1e-5:
log.note('RHF/RKS wavefunction has an internal instablity')
mo = _rotate_mo(mf.mo_coeff, mf.mo_occ, v)
else:
log.note(
'RHF/RKS wavefunction is stable in the internal stability analysis'
)
mo = mf.mo_coeff
return mo
def rhf_internal(mf, with_symmetry=True, verbose=None):
log = logger.new_logger(mf, verbose)
g, hop, hdiag = newton_ah.gen_g_hop_rhf(mf,
mf.mo_coeff,
mf.mo_occ,
with_symmetry=with_symmetry)
def precond(dx, e, x0):
hdiagd = hdiag - e
hdiagd[abs(hdiagd) < 1e-8] = 1e-8
return dx / hdiagd
x0 = numpy.zeros_like(g)
x0[g != 0] = 1. / hdiag[g != 0]
if not with_symmetry: # allow to break point group symmetry
x0[numpy.argmin(hdiag)] = 1
e, v = lib.davidson(hop, x0, precond, tol=1e-4, verbose=log)
if e < -1e-5:
log.note('RHF/RKS wavefunction has an internal instablity')
mo = _rotate_mo(mf.mo_coeff, mf.mo_occ, v)
else:
log.note(
'RHF/RKS wavefunction is stable in the intenral stablity analysis')
mo = mf.mo_coeff
return mo
def rhf_internal(mf, with_symmetry=True, verbose=None):
log = logger.new_logger(mf, verbose)
g, hop, hdiag = newton_ah.gen_g_hop_rhf(mf, mf.mo_coeff, mf.mo_occ,
with_symmetry=with_symmetry)
hdiag *= 2
def precond(dx, e, x0):
hdiagd = hdiag - e
hdiagd[abs(hdiagd)<1e-8] = 1e-8
return dx/hdiagd
# The results of hop(x) corresponds to a displacement that reduces
# gradients g. It is the vir-occ block of the matrix vector product
# (Hessian*x). The occ-vir block equals to x2.T.conj(). The overall
# Hessian for internal reotation is x2 + x2.T.conj(). This is
# the reason we apply (.real * 2) below
def hessian_x(x):
return hop(x).real * 2
x0 = numpy.zeros_like(g)
x0[g!=0] = 1. / hdiag[g!=0]
if not with_symmetry: # allow to break point group symmetry
x0[numpy.argmin(hdiag)] = 1
e, v = lib.davidson(hessian_x, x0, precond, tol=1e-4, verbose=log)
if e < -1e-5:
log.note('RHF/RKS wavefunction has an internal instablity')
mo = _rotate_mo(mf.mo_coeff, mf.mo_occ, v)
else:
log.note('RHF/RKS wavefunction is stable in the intenral stability analysis')
mo = mf.mo_coeff
return mo
def gen_g_hop (self, mo_coeff, mo_occ, fock_ao=None, h1e=None,
with_symmetry=True):
nmo = len (mo_occ)
idx_down = np.where (mo_occ==2)[0]
idx_up = np.where (mo_occ==2)[0]
idx_down[:self.ncore] = idx_up[:self.ncore] = False
nocc = self.ncore + self.nfroz
idx_down[nocc:] = idx_up[nocc:] = False
mo_occ[idx_down] -= 1e-4
mo_occ[idx_up] += 1e-4
rets = gen_g_hop_rhf (self, mo_coeff, mo_occ, fock_ao=fock_ao, h1e=h1e, with_symmetry=with_symmetry)
mo_occ[idx_up] -= 1e-4
mo_occ[idx_down] += 1e-4
return rets